On the Performance of Cyber-Biomedical Features for Intrusion Detection in Healthcare 5.0

  • Pedro H. Lui UFSM
  • Lucas P. Siqueira UFSM
  • Juliano F. Kazienko UFSM
  • Vagner E. Quincozes UFF
  • Silvio E. Quincozes UNIPAMPA
  • Daniel Welfer UFSM

Resumo


Healthcare 5.0 integrates Artificial Intelligence (AI), the Internet of Things (IoT), real-time monitoring, and human-centered design toward personalized medicine and predictive diagnostics. However, the increasing reliance on interconnected medical technologies exposes them to cyber threats. Meanwhile, current AI-driven cybersecurity models often neglect biomedical data, limiting their effectiveness and interpretability. This study addresses this gap by applying eXplainable AI (XAI) to a Healthcare 5.0 dataset that integrates network traffic and biomedical sensor data. Classification outputs indicate that XGBoost achieved 99% F1-score for benign and data alteration, and 81% for spoofing. Explainability findings reveal that network data play a dominant role in intrusion detection whereas biomedical features contributed to spoofing detection, with temperature reaching a Shapley values magnitude of 0.37.

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Publicado
09/06/2025
LUI, Pedro H.; SIQUEIRA, Lucas P.; KAZIENKO, Juliano F.; QUINCOZES, Vagner E.; QUINCOZES, Silvio E.; WELFER, Daniel. On the Performance of Cyber-Biomedical Features for Intrusion Detection in Healthcare 5.0. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 25. , 2025, Porto Alegre/RS. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2025 . p. 389-400. ISSN 2763-8952. DOI: https://doi.org/10.5753/sbcas.2025.7182.

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